Comparison of Several Simplistic High-Level Approaches for Estimating the Global Energy and Electricity Use of ICT Networks and Data Centers (Pages: 50-63)

Anders S. G. Andrae

Huawei Technologies Sweden AB, Skalholtsgatan 9, 16494 Kista, Sweden

DOI: https://doi.org/10.30634/2414-2077.2019.05.06

 

 

 


Abstract:
Currently the global energy and electricity use of ICT networks and data centers are estimated and predicted by several different top-down approaches. It has not been investigated which prediction approach best answers to the 5G, Artificial Intelligence and Internet of Things megatrends which are expected to emerge until 2030 and beyond. The analysis of the potential correlation between storage volume, communication volume and computations (instructions, operations, bits) is also lacking. The present research shows that several different activity metrics (AM) – e.g. data traffic, subscribers, capita, operations – have and can be been used. First the global baseline electricity evolution (TWh) for 2010, 2015 and 2020 for networks of fixed, mobile and data centers is set based on literature. Then the respective AM – e.g. data traffic – associated with each network are identified. Then the following are proposed: Compound Aggregated Growth Rate (CAGR) for each AM, CAGR for TWh/AM and the resulting TWh values for 2025 and 2030. The results show that AMs based on data traffic are best suited for predicting future TWh usage of networks. Data traffic is a more robust (scientific) AM to be used for prediction than subscribers as the latter is a more variable and less definable concept. Nevertheless, subscriber based AM are more uncertain than data traffic AM as the subscriber is neither a well-defined unit, nor related to the network equipment which handle the data. Despite large non-chaotic uncertainties, data traffic is a better AM than subscribers for expressing the energy evolution of ICT Networks and Data Centers. Top-down/high-level models based on data traffic are sensitive to the amount of traffic however also to the development of future electricity intensity. For the first time the primary energy use of computing, resulting from total global instructions and energy per instruction, is estimated.

Combining all networks and data centers and using one AM for all does not reflect the evolution improvement of individual network types. Very simplistic high-level estimation models tend to both overestimate and underestimate the TWh. However, looking at networks and data centers as one big entity better reflects the future converging paradigm of telecom, ICT and computing.

The next step is to make the prediction models more sophisticated by using equipment standards instead of top-down metrics. The links between individual equipment roadmaps (e.g. W/(bits per second)) and sector-level roadmaps need further study.

Keywords: ICT, data centers, data traffic, model, communication networks, electricity, prediction, primary energy, subscribers.